Towards Machine Learning Supported Smart Parking Solutions

Population growth coupled with the trend of urbanization calls for smart city applications that comprise intelligent parking solutions. Most of already existing solutions require significant investments at installation, while the problem of counting free parking spots on an image is easily executed by a human. Hence, we considered computer vision applications supported by machine learning to mimic the human behavior on this task.
We approached the problem by cropping the camera images monitoring numerous spots into images corresponding to the individual parking slots. Then we fed these images to several neural network architectures to find a well-performing model that reliably makes a distinction between free and occupied parking places.
As a result of the project, we significantly reduced the classification error rate compared to the initial model and were able to test the applicability of different approaches for classifying occluded spots.